Researchers have developed a new smartphone app that allows users to identify up to 500 bird species through uploaded photos.

Scientists at Columbia University School of Engineering and Applied Science, led by Computer Science Professor Peter Belhumeur, used computer vision and machine learning techniques, to develop the app Birdsnap.

The app is an electronic field guide featuring 500 of the most common North American bird species.

The app enables users to identify bird species through uploaded photos and accompanies a comprehensive website that includes some 50,000 images.

Birdsnap, which also features birdcalls for each species, offers users numerous ways to organise species - alphabetically, by their relationship in the Tree of Life, and by the frequency with which they are sighted at a particular place and season.

The researchers, who collaborated with colleagues at the University of Maryland, are presenting their work at the IEEE Conference on Computer Vision and Pattern Recognition in Columbus.

"Our goal is to use computer vision and artificial intelligence to create a digital field guide that will help people learn to recognise birds," said Belhumeur, who launched Leafsnap, a similar electronic field guide for trees, with colleagues two years ago.

"We've been able to take an incredible collection of data - thousands of photos of birds - and use technology to organise the data in a useful and fun way," said Belhumeur.

Belhumeur and his colleague, Computer Science Professor David Jacobs of the University of Maryland, realised that many of the techniques they have developed for face recognition, in work spanning more than a decade, could also be applied to automatic species identification.

State-of-the-art face recognition algorithms rely on methods that find correspondences between comparable parts of different faces, so that, for example, a nose is compared to a nose, and an eye to an eye.

Birdsnap works the same way, detecting the parts of a bird so that it can examine the visual similarity of its comparable parts (each species is labelled through the location of 17 parts).

It automatically discovers visually similar species and makes visual suggestions for how they can be distinguished.

Belhumeur hopes next to work with Columbia Engineering colleagues on adding the ability to recognise bird songs, bringing audio and visual recognition together.

He also wants to create "smart" binoculars that use this artificial intelligence technology to identify and tag species within the field of view.